1065 results about "Data breach" patented technology

The invention discloses a dynamic authority management system and method based on multi-source salary data integration, and relates to the technical field of salary data management, and the method comprises the following steps: collecting role names, authority boundaries and operation granularity information from a plurality of business systems, and generating structured role semantic ontology entries based on a field-level semantic annotation mode; and disassembling the to-be-mapped role according to the authority dimension, calculating a semantic similarity index between the to-be-mapped role and the standard role based on the role semantic ontology library, and outputting a role consistency scoring matrix. According to the method, the authority difference is identified through the role semantic ontology and the similarity score, the minimum authorization judgment is realized in combination with the rule engine and reinforcement learning, the audit traceability and anomaly detection are guaranteed by using the block chain and the dynamic graph, and finally a strategy self-evolution closed loop is constructed. And the problems of permission mismatching and data leakage caused by role semantic inconsistency are effectively solved.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention discloses a financial privacy security alignment method and system based on federated learning and adversarial training, and the method comprises the steps: enabling a plurality of financial institution clients to obtain local financial data, and carrying out the semantic analysis and sensitivity grading; constructing a federated learning framework, performing homomorphic encryption on a local model gradient by adopting Paillier, uploading the local model gradient to an aggregation server for secure aggregation, and generating global gradient update parameters; dynamically selecting a desensitization strategy based on the business scene weight and the data sensitivity, and executing field-level desensitization processing on the financial data; an adversarial sample is injected in local model training, and a task loss function and an adversarial loss function are jointly optimized; and mapping compliance terms into technical rules, and completing privacy protection and compliance alignment. According to the method, data privacy protection is enhanced, the data leakage risk is effectively reduced, and financial data privacy security is ensured. Safe transmission and effective aggregation of data are ensured, and the global model training efficiency and safety are improved. An adversarial sample is injected, and high accuracy and stability are kept.

The invention provides a credible sharing method and system for multi-party industrial data, and relates to the technical field of data processing, and the method comprises the steps that multiple participants encrypt local industrial data through a credible execution space, and generate encrypted data and corresponding data fingerprints; defining a data access rule based on the smart contract; verifying and auditing the data access request, and obtaining an encrypted data authorization certificate after verification and auditing; and under the federated learning framework, the trusted execution space of each data provider executes the privacy calculation task based on the authorization certificate, and outputs a task calculation result and sends the task calculation result to the task requester. According to the method and the device, the technical problem of privacy risk of data leakage or tampering caused by contradiction between multi-party industrial data sharing and privacy protection is solved, the privacy can still be ensured while data sharing is ensured through federated learning and trusted execution space, and the security and the credibility of data sharing are improved.

The invention relates to a health data encryption storage method and device, equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed health data, carrying out the sensitivity analysis and grade division of the health data, and obtaining health data sub-blocks; performing encryption preprocessing on the health data sub-blocks, and performing group data protection according to a preset differential privacy algorithm to obtain encrypted data blocks; generating an initial master key through a hardware security module, and performing derived hierarchical encryption on the encrypted data block to obtain a data encryption key; performing fragmentation processing and regular distributed storage on the data encryption key to obtain a dynamic security key; and performing metadata separation storage on the encrypted data according to the dynamic security key, and performing data permission determination according to a preset role-based access control mechanism to obtain an encrypted isolation database. According to the invention, efficient security protection can be maintained under different application scenes and access permissions, and the risk of data leakage is reduced.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise-level large-model agent application system supporting multi-modal collaboration, and the system comprises a user interaction terminal, an agent engine server, a knowledge engine server, a plug-in integration center, and a distributed storage unit. The agent engine server is responsible for intention recognition and task arrangement of a multi-modal input signal, and dynamically loads a differential reasoning strategy based on an environment isolation mechanism. And the knowledge engine server constructs a cross-modal semantic anchor point, analyzes an unstructured document into a tetrad knowledge unit, and realizes accurate recall of images and texts by using a hybrid retrieval algorithm. And the plug-in integration center executes outbound replacement and inbound restoration of the sensitive data through the context-aware dynamic desensitization gateway. According to the method, through a multi-modal semantic association and closed-loop verification mechanism, the problems of low complex document retrieval precision and leakage of external calling data are solved, and the service processing capacity and safety of the system are improved.

The invention provides a water conservancy information security protection method and system. The method comprises the following steps: S1, edge equipment physical security reinforcement; S2, dynamic encrypted data acquisition; S3, adaptive fragmentation redundancy transmission; a protection module is deployed in a water conservancy sensor and an RTU, equipment vibration, uncovering and environmental parameters are monitored, abnormal physical attacks or environmental overrun are detected, a secret key is automatically erased, a data protection mode is started, data leakage when equipment is physically damaged is prevented, data security of edge equipment is guaranteed, and the safety of the equipment is improved. And in cross-system data interaction, an MPC or federated learning technology is adopted, original data is locally reserved, and a block chain audit log is generated to record a calling behavior, so that data sharing is realized, data is ensured not to be locally out, data privacy is guaranteed, and a data calling tracing basis is provided.

The invention discloses a smart home early warning method and system based on dual authentication, and relates to the technical field of smart home. Four-element data of an early warning event is collected through an edge computing gateway, the four-element data is coded and packaged into a data packet, and the data packet is transmitted to a cache region of the edge computing gateway for further processing; according to the smart home early warning system based on dual authentication, through an equipment identity dual authentication mechanism, the security of equipment access is doubly guaranteed from a physical layer and a logic layer, intrusion of unknown equipment is effectively prevented, the risk of data leakage is reduced, in the aspect of data storage, an on-chain and off-chain collaborative storage mode is adopted, and the security of equipment access is improved. The authenticity and integrity of event key information are ensured by using the non-tampering characteristic of the block chain on the chain, and the security of original data is protected through distributed storage and encryption under the chain, so that the privacy and security of user data are protected comprehensively, and the security of the data of the smart home system is ensured.

The invention relates to an alliance chain-based energy data sharing method and device, and a medium, and the method comprises the steps: collecting and preprocessing energy data based on a TEE, encrypting the data in the TEE, uploading the data to an IPFS, generating a hash value, and binding a data owner identifier; when the verification request is responded, integrity verification, numerical value verification and other operations are executed, and an encryption result is generated and returned to the demand side. After verification is passed, copyright information is embedded according to a data type matching watermarking algorithm, the IPFS is re-encrypted and uploaded, and a shared hash value is obtained. And storing the hash value and the decryption key into the block chain through the consensus node of the alliance chain, and transmitting the hash value and the decryption key to the demand side. And after the demand side decrypts and obtains the data, extracting the watermark and comparing the watermark with the on-chain parameters for verification. According to the method, sharing and verification of the data in an encryption state are ensured, privacy protection and use controllability are considered, and the problems of data leakage, tampering and ownership tracing in traditional sharing are solved.

The invention discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The present invention relates to systems and methods for proactive real-time anomaly detection in cross-environment RPC (Remote Procedure Call) communications within computing systems. Utilizing an Intelligent GraphRPC Method, this invention integrates advanced graph analysis techniques to enhance fault detection and workflow management. The method features a dual-graph approach, employing both real-time and aggregated dependency graphs, which allows for continuous monitoring and analysis of RPC interactions to detect and prevent unauthorized or misconfigured RPC calls between staging and production environments. An ingestion pipeline further supports the system by aggregating and archiving call graph data, providing beneficial insights into service dependencies and potential security risks. This proactive anomaly detection system is designed to seamlessly integrate into existing monitoring and alerting frameworks, providing a robust solution to safeguard data integrity and operational stability, thereby minimizing losses and reputational damage due to data breaches and system disruptions.

The invention discloses an identity authentication and control method and device based on a block chain. According to the method, biological features are collected through edge nodes to generate feature vectors, a combined hash value is generated in combination with a device scene label and a service role label, the combined hash value is uploaded to a cloud server after encrypted signature, and the combined hash value is written into a block chain through an intelligent contract; and during user authentication, the edge node calculates a real-time risk level, dynamically selects an authentication mode, calls an intelligent contract for verification after verification of the cloud server, and adjusts the risk level according to a verification result to implement management and control. According to the method, the edge cloud collaborative architecture is adopted, and the dynamic multi-factor authentication mechanism and the block chain consensus technology are combined, so that high-security privacy protection authentication is realized, the security requirements of different service scenes can be met, data leakage and replay attacks are effectively prevented, and the authentication efficiency and reliability are improved.

The invention discloses a multi-level access control and authority synchronization method for secure mobile storage, and belongs to the field of mobile storage security. Aiming at the problems of data leakage, poor authority management and the like of the mobile storage device, a multi-level access control framework, a decentralized authority synchronization mechanism and a dynamic token and zero-knowledge proof system are constructed. Generating a plurality of layers of sub-keys from a master key by using a PBKDF2 algorithm through hierarchical key generation, and associating different encryption areas and authority strategies; incremental synchronization is carried out based on a Merkle tree, and data consistency is guaranteed through a consistency verification protocol; a temporary token is generated by using a Schnorr protocol, and the permission is verified in combination with zero-knowledge proof, so that the privacy security is ensured. In scene application of a power grid and the like, a master key is activated through biological recognition, permission is loaded according to a strategy, and permission change is efficiently synchronized. According to the method, the mobile storage security and the management efficiency are improved, and accurate authority control and reliable synchronization are realized.

The invention discloses a diabetic complication monitoring auxiliary system and method in the technical field of complication monitoring, and the system comprises a data collection module which is used for obtaining local multi-modal data of a user in real time; the federal learning module is used for integrating the local multi-modal data of the user with other anonymous medical databases through a distributed architecture and constructing a multi-family cross-institution complication prediction model; the causal reasoning module is used for analyzing the causal relationship among the blood glucose fluctuation, the glycosylated hemoglobin and the target complication based on an anti-factual causal reasoning algorithm, and outputting a causal effect value to correct the prediction deviation; the prediction module is used for generating occurrence probabilities of future specific complications based on the causal effect values and risk scores output by the complication prediction model, and classifying the occurrence probabilities according to risk levels; and the early warning module is used for generating personalized intervention suggestions according to the risk levels. According to the scheme, the data leakage risk in the data acquisition process is reduced through the federal learning distributed architecture, and the prediction reliability is improved.

The invention relates to the technical field of information security, and particularly provides a medical Web service multilayer dynamic protection method and system, and the method comprises the steps: obtaining access source information, terminal equipment information and user behavior information when a user initiates a data access request; obtaining an access risk score for representing a current access risk level according to the access source information, the terminal equipment information and the user behavior information; querying a pre-constructed mapping relation table about the risk score and the risk strategy according to the risk score, so as to obtain an access permission and a data display strategy for the current data access request; performing data processing on medical data which is prepared to be returned and corresponds to the data access request according to the access permission and the data display strategy, and then sending the medical data to the user terminal; the method can effectively solve the problems that high data leakage risk events cannot be processed and medical data leaks due to the fact that real-time and dynamic data leakage risk assessment cannot be carried out.

The invention discloses an autoregressive language model training process-oriented data leakage risk quantification method, which comprises the following steps of: executing enhanced member data division processing on an original training text set, and generating a ternary partition text set containing member, non-member and key boundary texts through an optimization function; for each text, using the target model to extract member attributes from three channels of forward reasoning, back propagation and state evolution, and fusing the member attributes into member attribute vectors; inputting the member attribute vector into a hierarchical comparison embedded network, and mapping the member attribute vector into an optimized embedded representation vector through comparison learning including similar clustering, heterogeneous separation and boundary positioning; and inputting the embedded representation vector into a dynamic reasoning classifier capable of perceiving a training stage, judging the risk membership degree of the dynamic reasoning classifier, and generating a data leakage risk quantification result. According to the invention, real-time and fine-grained dynamic quantification can be carried out on the leakage risk, and timely early warning is provided for model training.

The invention discloses an electric power artificial intelligence model security protection method and system based on a zero-trust architecture, and belongs to the technical field of power grid security protection. The method comprises the following steps: constructing an encryption service request according to identity recognition information and environment information of an access user; verifying the request by adopting a multi-factor identity verification method, and generating an authentication credible score; performing weighted calculation by combining the user historical behavior score, the equipment safety score and the environmental factor score to obtain a dynamic comprehensive credibility score; and generating a permission configuration strategy based on the score to realize fine-grained access control. The system comprises a user request access module, an information collection module, a credibility evaluation module and an access control module, and through continuous verification and dynamic permission adjustment, safe access of the electric power artificial intelligence model is ensured. The defect that a traditional security technology depends on static authentication and boundary protection is overcome, data leakage, model tampering and illegal access are effectively prevented, and the overall security and stability of a power system are improved.

The invention relates to the related technical field of zero-trust networks, in particular to a network data security protection method and system based on multi-protection right control, and the method comprises the steps: connecting a terminal and a protection center, setting a primary authorization mechanism, starting hierarchical authorization of a right group, evaluating the risk in real time, interrupting the access if the risk does not accord with the threshold, and carrying out the threat sharing. The technical problems that the authority is allocated only according to a fixed role, the enterprise business scene change and the user actual demand change cannot be adapted, the access of the user to the isolated data area resource is lack of fine-grained control, the situation of excessive authority granting or insufficient authority granting is easy to occur, and the data leakage risk is increased are solved; according to the invention, dynamic hierarchical management of the authority is realized by constructing a primary authorization authentication mechanism and a hierarchical authorization authentication mechanism, the authority is minimized to a single service instance, and fine-grained partitioning is carried out on resources, so that data access control is more accurate, illegal data access and attack diffusion are effectively blocked, and data access efficiency is improved. And the safety of the isolated data area is ensured.

The invention discloses a strong-isolation private domain agent data processing method and server, and relates to the field of data security, and the method comprises the steps: receiving a data processing request sent by a user through a user terminal, and obtaining a department identifier; according to the department identification, the data processing request is routed to a corresponding internal data interface module, the internal data interface module is used for analyzing the data processing request, obtaining a processing task and calling an intelligent agent engine, and the intelligent agent engine is used for obtaining a temporary access credential according to the department identification and sending the temporary access credential to the corresponding internal data interface module; accessing an internal data storage area of the department based on the temporary access credential to obtain to-be-processed data; and the agent engine is used for executing the processing task, analyzing and processing the to-be-processed data to obtain a processing result, and transmitting the processing result to the user terminal through the output interface module. According to the application, the risk of data leakage and the risk of unauthorized access and information leakage among different departments can be effectively reduced.

The invention relates to the field of electric power system information communication networks, in particular to a safety protection system based on an electric power system information communication network, which comprises a terminal safety access module used for carrying out identity authentication and access control on external terminal equipment in the electric power system information communication network, preventing illegal terminals from accessing the network, and sending the terminal safety access module to the terminal safety access module. And the network communication protection module is used for performing protocol analysis on the communication flow in the power system information communication network. Through cooperative work of four layers of modules of global coverage, closed-loop protection, terminal, network, service and operation, a global scene of an information communication network of a power system is covered, a'monitoring-analysis-response-defense 'closed loop is formed, no dead corner of security protection is realized, the security risk is remarkably reduced, the high-risk attack blocking success rate is improved, the data leakage risk is reduced, and the security and protection efficiency is improved. And the security event discovery time is shortened, and the anti-attack capability and the risk resistance level of the power system are comprehensively improved.

The invention provides a secure sandbox system of a trusted data space, which comprises a data source module, a data secure sandbox platform module and an interaction layer module, the method comprises the steps that a data source module is used for encrypting data, encrypted data is transmitted to a data security sandbox platform module through an encryption transmission channel, and only a data source administrator has the authority to decrypt and calculate, and relates to the technical field of data security. Multiple security defense lines are constructed for the data, the data are always in an encrypted state in the circulation process, different operation environments are isolated from one another, and the risk of data leakage is greatly reduced.

The invention discloses an airport operation and maintenance data storage method and device based on a block chain and electronic equipment, and relates to the field of airport operation and maintenance data security management.The method comprises the steps that a first hash value is calculated for collected original operation and maintenance data, and a probe private key is used for conducting double-layer digital signature, transmitting the signed operation and maintenance data packet to an intelligent operation and maintenance system by adopting a preset encryption communication channel, calling a block chain intelligent contract through the intelligent operation and maintenance system under the condition that the integrity verification of the single piece of original operation and maintenance data is passed, and verifying the legality of the double-layer digital signature; and performing structured processing on the original operation and maintenance data passing the signature verification, and storing the original operation and maintenance data to a block chain network. According to the method and the device, the technical problems that data among multi-source heterogeneous systems is insufficient in credibility in a management process of airport operation and maintenance data, the security of data transmission cannot be guaranteed, and data leakage is easily caused in related technologies are solved.

The invention provides a large model security access system and method. The method comprises the following steps: receiving a dialogue request sent by a security client through WebSocket connection and executing security detection; if the security detection is passed, forwarding the dialogue request to an API interface of a target large model service requested by the security client; and receiving response content returned by the target large model service, performing security processing on the response content, and sending the response content subjected to the security processing to the security client. In the method, on one hand, the gateway transmits data through encrypted WebSocket connection to ensure the security of the data transmission and processing process and avoid the risk of data leakage, and on the other hand, the gateway performs security detection and processing on the request and response according to the security policy issued by the security management platform to prevent the leakage of malicious prompt words and sensitive information and the generation of harmful content. And the content is ensured to meet safety and compliance requirements.

The invention relates to the technical field of power data transmission, and discloses a power data key negotiation and symmetric encryption method, system and device and a medium, and the method comprises the steps: obtaining target power data, and carrying out the first preprocessing of the target power data; performing second preprocessing on the target power data; obtaining a target statistical feature of the gray matrix according to the first preprocessing result, and generating a first replacement structure in combination with a target shared key obtained after the second preprocessing; and encrypting the target power data according to the first replacement structure. According to the invention, the security of the power data is greatly improved. In the power data transmission process, through the method of the invention, it can be ensured that data is not stolen or tampered in the transmission process, and the risk of data leakage is effectively prevented. Meanwhile, the implementation steps of the method are simple and clear, the method is easy to popularize and apply in various power systems, and a powerful guarantee is provided for stable operation of the power systems.

The invention discloses a digital bond risk assessment method and system based on a block chain and privacy calculation, electronic equipment and a storage medium, and the method achieves the innovation of digital bond risk assessment, and has the core effect of enhancing the data security and privacy protection in the assessment process. And meanwhile, the integrity and non-tampering property of the data are ensured. According to the method, the homomorphic encryption technology is utilized, the encryption state of the data in the processing process can be ensured even if calculation is carried out at the cloud, and data leakage and unauthorized access are effectively prevented. Besides, the evaluation system provided by the invention greatly improves the transparency and credibility of evaluation, so that investors can more accurately grasp the risk condition of the digital bond, thereby promoting the healthy development of the digital financial market and the efficient configuration of capital. Through the intelligent evaluation process, the evaluation efficiency is remarkably improved, the decision-making period is shortened, and the capacity of quickly responding to market changes is provided for issuers and investors.

The invention discloses a data security communication system of the Internet of Things, which relates to the technical field of data security communication of the Internet of Things, and comprises the steps of equipment identity authentication, adoption of a Hash encryption and challenge response mechanism, dynamic key negotiation and combination with a Diffie-Hellman algorithm and an equipment operation state to generate a session key; the data encryption transmission uses AES-256 encryption and is transmitted through a TLS1.3 protocol, and abnormal traffic detection and prediction are carried out through DBSCAN clustering and an LSTM model; the data integrity is ensured through double verification of the receiving end; flexible access authority control is realized based on attributes in combination with fuzzy logic; recording an operation log and intelligently analyzing to complete safety audit; secret key updating is triggered according to a period or threat, and a new secret key is protected by a digital signature. According to the method, illegal access is prevented through multiple authentication, confidentiality is enhanced through a dynamic key, threats are intelligently detected, authority is flexibly controlled, logs are effectively audited, the key is updated in time, vulnerabilities are repaired, data leakage and attack loss are reduced, and a safety barrier is built for application of the Internet of Things.